Beam losses due to the foil scattering for CSNS/RCS

For the Rapid Cycling Synchrotron of China Spallation Neutron Source (CSNS/RCS), the stripping foil scattering generates the beam halo and gives rise to additional beam losses during the injection process. The interaction between the proton beam and the stripping foil was discussed and the foil scattering was studied. A simple model and the realistic situation of the foil scattering were considered. By using the codes ORBIT and FLUKA, the multi-turn phase space painting injection process with the stripping foil scattering for CSNS/RCS was simulated and the beam losses due to the foil scattering were obtained.Comment: Submitted to HB2012, IHEP, Beijing, Sep. 17-21, 201

Anomaly Detection via Minimum Likelihood Generative Adversarial Networks

Anomaly detection aims to detect abnormal events by a model of normality. It plays an important role in many domains such as network intrusion detection, criminal activity identity and so on. With the rapidly growing size of accessible training data and high computation capacities, deep learning based anomaly detection has become more and more popular. In this paper, a new domain-based anomaly detection method based on generative adversarial networks (GAN) is proposed. Minimum likelihood regularization is proposed to make the generator produce more anomalies and prevent it from converging to normal data distribution. Proper ensemble of anomaly scores is shown to improve the stability of discriminator effectively. The proposed method has achieved significant improvement than other anomaly detection methods on Cifar10 and UCI datasets

Spectral Network Embedding: A Fast and Scalable Method via Sparsity

Network embedding aims to learn low-dimensional representations of nodes in a network, while the network structure and inherent properties are preserved. It has attracted tremendous attention recently due to significant progress in downstream network learning tasks, such as node classification, link prediction, and visualization. However, most existing network embedding methods suffer from the expensive computations due to the large volume of networks. In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis. In Progle, we first construct a \textit{sparse} proximity matrix and train the network embedding efficiently via sparse matrix decomposition. Then we introduce a network propagation pattern via spectral analysis to incorporate local and global structure information into the embedding. Besides, this model can be generalized to integrate network information into other insufficiently trained embeddings at speed. Benefiting from sparse spectral network embedding, our experiment on four different datasets shows that Progle outperforms or is comparable to state-of-the-art unsupervised comparison approaches---DeepWalk, LINE, node2vec, GraRep, and HOPE, regarding accuracy, while is $10\times$ faster than the fastest word2vec-based method. Finally, we validate the scalability of Progle both in real large-scale networks and multiple scales of synthetic networks

Accelerated Schemes for the L1/L2L_1/L_2 Minimization

In this paper, we consider the $L_1/L_2$ minimization for sparse recovery and study its relationship with the $L_1$-$\alpha L_2$ model. Based on this relationship, we propose three numerical algorithms to minimize this ratio model, two of which work as adaptive schemes and greatly reduce the computation time. Focusing on two adaptive schemes, we discuss their connection to existing approaches and analyze their convergence. The experimental results demonstrate the proposed approaches are comparable to the state-of-the-art methods in sparse recovery and work particularly well when the ground-truth signal has a high dynamic range. Lastly, we reveal some empirical evidence on the exact $L_1$ recovery under various combinations of sparsity, coherence, and dynamic ranges, which calls for theoretical justification in the future.Comment: 10 page

Sharp Inequalities between Harmonic, Seiffert, Quadratic and Contraharmonic Means

In this paper, we present the greatest values $\alpha$, $\lambda$ and $p$, and the least values $\beta$, $\mu$ and $q$ such that the double inequalities $\alpha D(a,b)+(1-\alpha)H(a,b)<T(a,b)<\beta D(a,b)+(1-\beta) H(a,b)$, $\lambda D(a,b)+(1-\lambda)H(a,b)<C(a,b)<\mu D(a,b)+(1-\mu) H(a,b)$ and $p D(a,b)+(1-p)H(a,b)0$ with $a\neq b$, where $H(a,b)=2ab/(a+b)$, $T(a,b)=(a-b)/[2\arctan((a-b)/(a+b))]$, $Q(a,b)=\sqrt{(a^2+b^2)/2}$, $C(a,b)=(a^2+b^2)/(a+b)$ and $D(a,b)=(a^3+b^3)/(a^2+b^2)$ are the harmonic, Seiffert, quadratic, first contraharmonic and second contraharmonic means of $a$ and $b$, respectively.Comment: 11 page

Unextendible maximally entangled bases in dxd

We investigate the unextendible maximally entangled bases in $\mathbb{C}^{d}\bigotimes\mathbb{C}^{d}$ and present a $30$-number UMEB construction in $\mathbb{C}^{6}\bigotimes\mathbb{C}^{6}$. For higher dimensional case, we show that for a given $N$-number UMEB in $\mathbb{C}^{d}\bigotimes\mathbb{C}^{d}$, there is a $\widetilde{N}$-number, $\widetilde{N}=(qd)^2-(d^2-N)$, UMEB in $\mathbb{C}^{qd}\bigotimes\mathbb{C}^{qd}$ for any $q\in\mathbb{N}$. As an example, for $\mathbb{C}^{12n}\bigotimes\mathbb{C}^{12n}$ systems, we show that there are at least two sets of UMEBs which are not equivalent.Comment: Errors correcte

On small set of one-way LOCC indistinguishability of maximally entangled states

In this paper, we study the one-way local operations and classical communication (LOCC) problem. In $\mathbb{C}^d\otimes\mathbb{C}^d$ with $d\geq4$, we construct a set of $3\lceil\sqrt{d}\rceil-1$ one-way LOCC indistinguishable maximally entangled states which are generalized Bell states. Moreover, we show that there are four maximally entangled states which cannot be perfectly distinguished by one-way LOCC measurements for any dimension $d\geq 4$.Comment: 10 pages.Very recently, we became aware of related work \cite{Zhang2} in which the same $\lceil\frac{d}{2}\rceil+2$ states in $\mathbb{C}^d\otimes\mathbb{C}^d$ is proved to be one-way LOCC indistinguishable. arXiv admin note: text overlap with arXiv:1310.4220 by other autho

A Combinatorial Method for Computing Characteristic Polynomials of Starlike Hypergraphs

By using the Poisson formula for resultants and the variants of chip-firing game on graphs, we provide a combinatorial method for computing a class of of resultants, i.e. the characteristic polynomials of the adjacency tensors of starlike hypergraphs including hyperpaths and hyperstars,which are given recursively and explicitly

Study on the injection optimization and transverse coupling for CSNS/RCS

The injection system of the China Spallation Neutron Source uses H- stripping and phase space painting method to fill large ring acceptance with the linac beam of small emittance. The emittance evolution, beam losses, and collimation efficiency during the injection procedures for different injection parameters, such as the injection emittances, starting injection time, twiss parameters and momentum spread, were studied, and then the optimized injection parameters was obtained. In addition, the phase space painting scheme which also affect the emittance evolution and beam losses were simulated and the optimization range of phase space painting were obtained. There will be wobble in the power supply of the injection bumps, and the wobble effects were presented. In order to study the transverse coupling, the injection procedures for different betatron tunes and momentum spreads were studied.Comment: Submitted to proceedings of IPAC2012, New Orleans, Louisiana, USA, May 20-25, 201

Hessian informed mirror descent

Inspired by the recent paper (L. Ying, Mirror descent algorithms for minimizing interacting free energy, Journal of Scientific Computing, 84 (2020), pp. 1-14),we explore the relationship between the mirror descent and the variable metric method. When the metric in the mirror decent is induced by a convex function, whose Hessian is close to the Hessian of the objective function, this method enjoys both robustness from the mirror descent and superlinear convergence for Newton type methods. When applied to a linearly constrained minimization problem, we prove the global and local convergence, both in the continuous and discrete settings. As applications, we compute the Wasserstein gradient flows and Cahn-Hillard equation with degenerate mobility. When formulating these problems using a minimizing movement scheme with respect to a variable metric, our mirror descent algorithm offers a fast convergent speed for the underlining optimization problem while maintaining the total mass and bounds of the solution